class_docstr: Class for holding configuration which is the partial analysis of the input sentence.
  The transition based parser aims at finding set of operators that transfer the initial
  configuration to the terminal configuration.
  
  The configuration includes:
      - Stack: for storing partially proceeded words
      - Buffer: for storing remaining input words
      - Set of arcs: for storing partially built dependency tree
  
  This class also provides a method to represent a configuration as list of features.
docstr: :param dep_graph: the representation of an input in the form of dependency graph.
  :type dep_graph: DependencyGraph where the dependencies are not specified.
docstr_tok: [":","param","dep_graph",":","the","representation","of","an","input","in","the","form","of","dependency","graph",".",":","type","dep_graph",":","DependencyGraph","where","the","dependencies","are","not","specified","."]
code: def __init__(self, dep_graph):
      """
      :param dep_graph: the representation of an input in the form of dependency graph.
      :type dep_graph: DependencyGraph where the dependencies are not specified.
      """
      # dep_graph.nodes contain list of token for a sentence
      self.stack = [0]  # The root element
      self.buffer = list(
          range(1, len(dep_graph.nodes))
      )  # The rest is in the buffer
      self.arcs = []  # empty set of arc
      self._tokens = dep_graph.nodes
      self._max_address = len(self.buffer)
  
imports: import pickle
  import tempfile
  from copy import deepcopy
  from operator import itemgetter
  from os import remove
  from nltk.parse import DependencyEvaluator, DependencyGraph, ParserI
label_desc: Use image node_id 1 to create a new Configuration object  with example: obj = Configuration(dep_graph)